3D datasets.
Current large-scale 2D image datasets offer three crucial components that benefit learning: scale, diversity, and realism. Ideally, models that reason about 3D objects should have access to datasets that meet these same criteria. However, of the numerous 3D object datasets that currently exist, none are able to excel in all three categories to the same degree as their 2D counterparts.
Datasets such as KIT[1]}, YCB[2]}, BigBIRD[3]}, IKEA[4]}, and Pix3D[5]} provide image-calibrated models over a diverse set of household objects, but severely lack in scale with only a few hundred objects at most. EGAD[6]} procedurally generates 2K objects for grasping, but produces objects that are not that realistic or diverse. Slightly larger datasets of photo-realistic objects include GSO[7]}, PhotoShape[8]}, ABO[9]} and 3D-Future[10]}, and ShapeNet[11]} with object counts in the tens of thousands, see Fig. REF  for comparisons between Objaverse and these datasets. Datasets for CAD models, such as ModelNet[12]} and DeepCAD[13]}, and ABC[14]} do not include textures or materials, which limits their ability to represent objects that could plausibly be found in the real world. Datasets of scanned 3D objects and environments are valuable for real-world understanding[15]}, [16]}, [17]}, [18]}, but are quite small and limited. In addition to containing numerous artist designed objects, Objaverse contains many scanned assets, making it a useful source of data for learning from real-world distributions.
